Semantic Representation Learning of Scientific Literature based on Adaptive Feature and Graph Neural Network
arXiv:2311.00296v2 Announce Type: replace Abstract: Because most scientific literature data are unlabeled, semantic representation learning based on unsupervised graphs has become crucial. To enrich scientific-literature features, this paper proposes a semantic representation learning method based on adaptive features and graph neural networks. By introducing adaptive feature processing, scientific-literature features are considered globally and locally. The graph attention mechanism weights and aggregates features of scientific documents connected by citation relations, so that correlations a
The proliferation of unlabeled scientific literature data necessitates more advanced semantic representation techniques to extract value and improve searchability, making this a timely area of research.
Improved semantic representation of scientific literature enables more efficient knowledge discovery, accelerates research, and could lead to breakthroughs by connecting disparate information.
This method potentially improves the ability of AI models to understand and utilize vast, unstructured scientific text, enhancing literature review processes and potentially accelerating scientific progress.
- · AI researchers (NLP)
- · Scientific publishers
- · Academic institutions
- · Drug discovery
Enhanced discovery of research connections and potentially overlooked scientific insights.
Accelerated pace of scientific breakthroughs across various disciplines due to better information synthesis.
New AI-powered tools that can generate hypotheses or design experiments based on comprehensive literature understanding.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL